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Colin Ware, John G.W. Kelley, and David Pilar

Considerable effort has gone into building numerical weather and ocean prediction models during the past 50 years. Less effort has gone into the visual representation of output from those forecast models and many of the techniques used are known to be ineffective. The effectiveness of a data display depends on how well critical patterns can be perceived. This paper outlines a set of perceptual principles for what makes a good representation of a 2D vector field and shows how these principles can be used for the portrayal of currents, winds, and waves. Examples are given from a series of evaluation studies that examine the optimal representation of these variables. The results suggest that for static graphic presentations, equally spaced streamlines may be optimal. If wind barbs are curved to follow streamlines, perception of local wind speed and direction as well as the overall pattern is improved. For animated portrayals of model output, animated streamlets can perceptually separate layers of information so that atmospheric pressure and surface temperature can clearly be shown simultaneously with surface winds.

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John G. W. Kelley, David W. Behringer, H. Jean Thiebaux, and Bhavani Balasubramaniyan


The real-time, three-dimensional, limited-area Coastal Ocean Forecast System (COFS) has been developed for the northwestern Atlantic Ocean and implemented at the National Centers for Environmental Prediction. COFS generates a daily nowcast and 1-day forecast of water level, temperature, salinity, and currents. Surface forcing is provided by 3-h forecasts from the National Weather Service's Eta Model, a mesoscale atmospheric prediction model. Lateral oceanic boundary conditions are based on climatic data. COFS assimilates in situ sea surface temperature (SST) observations and multichannel satellite SST retrievals for the past 48 h. SST predictions from the assimilating and nonassimilating versions of COFS were compared with independent observations and a 14-km-resolution multichannel SST analysis. The assimilation of SST data reduced the magnitude and the geographic extent of COFS's characteristic positive temperature bias north of the Gulf Stream. The root-mean-square SST differences between the COFS predictions and in situ observations were reduced by up to 47%–50%. Qualitative comparisons were also made between predictions from the assimilating and nonassimilating versions and thermal profiles measured by expendable bathythermographs. These comparisons indicated that the assimilation scheme had positive impact in reducing temperature differences in the top 300 m at most locations. However, the subsurface comparisons also show that, in dynamically complex regions such as the Gulf Stream, the continental slope, or the Gulf of Maine, the data assimilation system has difficulty reproducing the observed ocean thermal structure and would likely benefit from the direct assimilation of observed profiles.

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John G. W. Kelley, Jay S. Hobgood, Keith W. Bedford, and David J. Schwab


A one-way coupled atmospheric–lake modeling system was developed to generate short-term, mesoscale lake circulation, water level, and temperature forecasts for Lake Erie. The coupled system consisted of the semi-operational versions of the Pennsylvania State University–National Center for Atmospheric Research three-dimensional, mesoscale meteorological model (MM4), and the three-dimensional lake circulation model of the Great Lakes Forecasting System (GLFS).

The coupled system was tested using archived MM4 36-h forecasts for three cases during 1992 and 1993. The cases were chosen to demonstrate and evaluate the forecasts produced by the coupled system during severe lake conditions and at different stages in the lake’s annual thermal cycle. For each case, the lake model was run for 36 h using surface heat and momentum fluxes derived from MM4’s hourly meteorological forecasts and surface water temperatures from the lake model. Evaluations of the lake forecasts were conducted by comparing forecasts to observations and lake model hindcasts.

Lake temperatures were generally predicted well by the coupled system. Below the surface, the forecasts depicted the evolution of the lake’s thermal structure, although not as rapidly as in the hindcasts. The greatest shortcomings were in the predictions of peak water levels and times of occurrence. The deficiencies in the lake forecasts were related primarily to wind direction errors and underestimation of surface wind speeds by the atmospheric model.

The three cases demonstrated both the potential and limitations of daily high-resolution lake forecasts for the Great Lakes. Twice daily or more frequent lake forecasts are now feasible for Lake Erie with the operational implementation of mesoscale atmospheric models such as the U.S. National Weather Service’s Eta Model and Rapid Update Cycle.

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John G. W. Kelley, Joseph M. Russo, J. Ronald Eyton, and Toby N. Carlson

A technique called Model Output Enhancement (MOE) has been developed for the generation and display of mesoscale weather forecasts. The MOE technique derives mesoscale or high-resolution (order of 1 km) weather forecasts from synoptic-scale numerical weather-prediction models by modifying model output with geophysical and land-cover data. Mesoscale forecasts generated by the MOE technique are displayed as color-class maps overlaid on perspective plots of terrain. The MOE technique has been demonstrated in the generation of mesoscale maximum-temperature and minimum-temperature forecasts for case-study days of clear-sky conditions over the Commonwealth of Pennsylvania. The generated forecasts were evaluated using data from selected climatological stations.

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Suzanne Van Cooten, Kevin E. Kelleher, Kenneth Howard, Jian Zhang, Jonathan J. Gourley, John S. Kain, Kodi Nemunaitis-Monroe, Zac Flamig, Heather Moser, Ami Arthur, Carrie Langston, Randall Kolar, Yang Hong, Kendra Dresback, Evan Tromble, Humberto Vergara, Richard A Luettich Jr., Brian Blanton, Howard Lander, Ken Galluppi, Jessica Proud Losego, Cheryl Ann Blain, Jack Thigpen, Katie Mosher, Darin Figurskey, Michael Moneypenny, Jonathan Blaes, Jeff Orrock, Rich Bandy, Carin Goodall, John G. W. Kelley, Jason Greenlaw, Micah Wengren, Dave Eslinger, Jeff Payne, Geno Olmi, John Feldt, John Schmidt, Todd Hamill, Robert Bacon, Robert Stickney, and Lundie Spence

The objective of the Coastal and Inland Flooding Observation and Warning (CI-FLOW) project is to prototype new hydrometeorologic techniques to address a critical NOAA service gap: routine total water level predictions for tidally influenced watersheds. Since February 2000, the project has focused on developing a coupled modeling system to accurately account for water at all locations in a coastal watershed by exchanging data between atmospheric, hydrologic, and hydrodynamic models. These simulations account for the quantity of water associated with waves, tides, storm surge, rivers, and rainfall, including interactions at the tidal/surge interface.

Within this project, CI-FLOW addresses the following goals: i) apply advanced weather and oceanographic monitoring and prediction techniques to the coastal environment; ii) prototype an automated hydrometeorologic data collection and prediction system; iii) facilitate interdisciplinary and multiorganizational collaborations; and iv) enhance techniques and technologies that improve actionable hydrologic/hydrodynamic information to reduce the impacts of coastal flooding. Results are presented for Hurricane Isabel (2003), Hurricane Earl (2010), and Tropical Storm Nicole (2010) for the Tar–Pamlico and Neuse River basins of North Carolina. This area was chosen, in part, because of the tremendous damage inflicted by Hurricanes Dennis and Floyd (1999). The vision is to transition CI-FLOW research findings and technologies to other U.S. coastal watersheds.

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